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Ritse M Mann

As seen in: Nature, Wiley Online Library, MDPI, The Lancet, BNR Nieuwsradio, EMJ- European Medical Journal
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Articles

MRICombo: a deep-learning-based framework for universal volumetric segmentation grading-staging and malignancy detection across heterogeneous MRI

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By Luyi Han, Dengqiang Jia, Yuanfeng Wei, Tianyu Zhang, Jiaju Huang, Da Wei Huang, Yapeng Wang, Shandong Wu, Ritse M Mann, Chenggong Yan, Yue Sun, Tao Tan, Zhuoneng Zhang, Michiel Kallenberg
| Nature Verified
Abstract Comprehensive magnetic resonance imaging (MRI) analysis in oncology involves multiple interrelated tasks including volumetric segmentation, grading, staging, and malignancy detection. However, most existing deep learning models are task-specific or sequence-specific, lacking the generalizability required for heterogeneous sequences.
Open in Who Shared Issue with article?

ERN GENTURIS cancer surveillance guideline for individuals with PTEN hamartoma tumour syndrome (PHTS)

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By Nicoline Hoogerbrugge, Ana Blatnik, Charlotte Lautrup, Robert Hüneburg, Daniela Turchetti, Frédéric Caux, Alex Teule, Jolanda H. Schieving, Inga-Lena Nilsson, Ritse M Mann, Per-Olof Lundgren, Thera P Links, Emma Tham, Sjaak Pouwels, Marc Tischkowitz, Anne van Altena, Kelly Kearley
| Nature Verified
Abstract PTEN hamartoma tumour syndrome (PHTS) is a diverse multi-system disorder predisposing to a high hereditary risk of breast, thyroid, endometrial, and a moderate risk of renal, and colorectal cancer and skin melanoma. Besides the risk of cancer, PHTS is also associated with benign tumours such as skin and connective tissue tumours, vascular malformations and neurodevelopmental disorders, including autism spectrum disorders.
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Development and internal validation of mammography feature-based prognostic models for distant recurrence-free survival of invasive breast cancer in a screening cohort

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By Jim Peters Verified, Ritse M Mann, Jonas Teuwen, Nico Karssemeijer, Esther H. Lips, Jelle Wesseling, Carla van Gils, Ruud Pijnappel, Linda de Munck, Marja van Oirsouw, Ellen Verschuur, Sjoerd G. Elias, Jos A.A.M. van Dijck, Nikita Moriakov, Merle M. van Leeuwen, Alexandra W. van den Belt-Dusebout, Mireille J. M. Broeders
| Nature Verified
Abstract Overdiagnosis in breast cancer screening may be reduced by identifying lesions that, although detected on screening mammograms, are unlikely to progress to poor outcomes and may not require recall. As a proof-of-concept, we evaluated prognostic models for 10-year distant recurrence-free survival (DRFS) using radiomics features from invasive breast cancers (IBCs) presenting as masses on screening mammograms.
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